In animal sciences, the number of published meta-analyses is increasing at a rate of 15% per year. This current review focuses on the good practices and the potential pitfalls in the conduct of meta-analyses in animal sciences, nutrition in particular. Once the study objectives have been defined, several key phases must be considered when doing a meta-analysis. First, as a principle of traceability, criteria used to select or discard publications should be clearly stated in a way that one could reproduce the final selection of data. Then, the coding phase, aiming to isolate specific experimental factors for an accurate graphical and statistical interpretation of the database, is discussed. Following this step, the study of the levels of independence of factors and of the degree of data balance of the meta-design represents an essential phase to ensure the validity of statistical processing. The consideration of the study effect as fixed or random must next be considered. It appears based on several examples that this choice does not generally have any influence on the conclusions of a meta-analysis when the number of experiments is sufficient.

Mechanistic models (MMs) have served as causal pathway analysis and ‘decision-support’ tools within animal production systems for decades. Such models quantitatively define how a biological system works based on causal relationships and use that cumulative biological knowledge to generate predictions and recommendations (in practice) and generate/evaluate hypotheses (in research). Their limitations revolve around obtaining sufficiently accurate inputs, user training and accuracy/precision of predictions on-farm. The new wave in digitalization technologies may negate some of these challenges. New data-driven (DD) modelling methods such as machine learning (ML) and deep learning (DL) examine patterns in data to produce accurate predictions (forecasting, classification of animals, etc.). The deluge of sensor data and new self-learning modelling techniques may address some of the limitations of traditional MM approaches – access to input data (e.g. sensors) and on-farm calibration. However, most of these new methods lack transparency in the reasoning behind predictions, in contrast to MM that have historically been used to translate knowledge into wisdom. The objective of this paper is to propose means to hybridize these two seemingly divergent methodologies to advance the models we use in animal production systems and support movement towards truly knowledge-based precision agriculture. In order to identify potential niches for models in animal production of the future, a cross-species (dairy, swine and poultry) examination of the current state of the art in MM and new DD methodologies (ML, DL analytics) is undertaken. We hypothesize that there are several ways via which synergy may be achieved to advance both our predictive capabilities and system understanding, being: (1) building and utilizing data streams (e.g. intake, rumination behaviour, rumen sensors, activity sensors, environmental sensors, cameras and near IR) to apply MM in real-time and/or with new resolution and capabilities; (2) hybridization of MM and DD approaches where, for example, a ML framework is augmented by MM-generated parameters or predicted outcomes and (3) hybridization of the MM and DD approaches, where biological bounds are placed on parameters within a MM framework, and the DD system parameterizes the MM for individual animals, farms or other such clusters of data. As animal systems modellers, we should expand our toolbox to explore new DD approaches and big data to find opportunities to increase understanding of biological systems, find new patterns in data and move the field towards intelligent, knowledge-based precision agriculture systems.

Technological and mathematical advances have provided opportunities to investigate new approaches for the holistic quantification of complex biological systems. One objective of these approaches, including the multi-inverse deterministic approach proposed in this paper, is to deepen the understanding of biological systems through the structural development of a useful, best-fitted inverse mechanistic model. The objective of the present work was to evaluate the capacity of a deterministic approach, that is, the multi-inverse approach (MIA), to yield meaningful quantitative nutritional information. To this end, a case study addressing the effect of diet composition on sheep weight was performed using data from a previous experiment on saccharina (a sugarcane byproduct), and an inverse deterministic model (named Paracoa) was developed. The MIA successfully revealed an increase in the final weight of sheep with an increase in the percentage of corn in the diet. Although the soluble fraction also increased with increasing corn percentage, the effective nonsoluble degradation increased fourfold, indicating that the increased weight gain resulted from the nonsoluble substrate. A profile likelihood analysis showed that the potential best-fitted model had identifiable parameters, and that the parameter relationships were affected by the type of data, number of parameters and model structure. It is necessary to apply the MIA to larger and/or more complex datasets to obtain a clearer understanding of its potential.

The dairy cow model ‘Molly’ is a mixed discrete event-continuous system model that simulates feeding, metabolism and lactation of dairy cows. Decades of model development have resulted in a valuable tool in dairy science. Due to the deprecation of the ACSL (Advanced Continuous Simulation Language) programming language, Molly has been translated into C++. This paper describes the translation process and discusses the advantages of the new implementation, one of which is the ability to run Molly within RStudio, a popular integrated development environment (IDE) for data science.

Feeding cattle with on-pasture supplementation or feedlot diets can increase animal efficiency and system profitability while minimizing environmental impacts. However, cattle system profit margins are relatively small and nutrient supply accounts for most of the costs. This paper introduces a nonlinear profit-maximizing diet formulation problem for beef cattle based on well-established predictive equations. Nonlinearity in predictive equations for nutrient requirements poses methodological challenges in the application of optimization techniques. In contrast to other widely used diet formulation methods, we develop a mathematical model that guarantees an exact solution for maximum profit diet formulations. Our method can efficiently solve an often-impractical nonlinear problem by solving a finite number of linear problems, that is, linear time complexity is achieved through parametric linear programming. Results show the impacts of choosing different objective functions (minimizing cost, maximizing profit and maximizing profit per daily weight gain) and how this may lead to different optimal solutions. In targeting improved ration formulation on feedlot systems, this paper demonstrates how profitability and nutritional constraints can be met as an important part of a sustainable intensification production strategy.

The rate of passage (ROP) in the gastrointestinal tract (GIT) influences the exposure time of food to the digestion and absorption processes. Consequently, ROP affects the efficiency of nutrient utilization and energy from the diet. This study aimed to determine the physiological parameters that characterize the digestive response, such as first appearance time (FAT), ROP, mean retention time (MRT) and transit time (TT) in adult Japanese quail (Coturnix coturnix japonica), and to evaluate the effects of sex, apparent metabolizable energy corrected for nitrogen balance (AMEn) content in the diet and different types of markers on these parameters. In the first trial, we investigated the effects of sex and AMEn level (high- and low-energy diet) on the FAT parameter. Thirty-two male and 32 female Japanese quail were randomly allocated to 8 battery cages and assigned to 4 treatments in a 2 × 2 factorial design with 4 replicates of 4 birds for each treatment. To determine the FAT, ferric oxide (1%) was added to the diet, and the excreta of the quail was monitored until the first appearance of the marker. The results indicated significant differences (P < 0.05) in the FAT between males (100 min) and females (56 min), regardless of the AMEn content. In the second trial, thirty-two 32-week-old female Japanese quail in the laying phase were assigned to four treatments in a 2 × 2 factorial design, in which the main independent variables were type of marker (Cr or Ti) and AMEn level (high- and low-energy diets). In order to determine ROP (ET1%), MRT and TT (ET100%), the markers (0.5%: Cr2O3 and 0.5%: TiO2) were added to the diets, and the excreta were collected for 750 min. The excretion times for 1% (ET1%), 25% (ET25%), 50% (ET50%), 75% (ET75%) and 100% (ET100%) were estimated using cumulative excretion curves. No effect was detected for the AMEn level (P > 0.05); however, the effect of different marker types was significant (P < 0.05). This difference increased with time and ET100% was estimated to occur at 59 min. The ROP was estimated to be 68 min. The TT was estimated to be 540 min using Cr and 599 min using Ti, with an average MRT value of 0930 h. Taken together, our findings support the hypothesis that Japanese quail digestion through the GIT can be dynamic and differ based on sex or marker type.

Knowing how energy intake is partitioned between maintenance, growth and egg production (EP) of birds makes it possible to structure models and recommend energy intakes based on differences in the BW, weight gain (WG) and EP on commercial quail farms. This research was a dose-response study to re-evaluate the energy partition for Japanese quails in the EP phase, based on the dilution technique to modify the retained energy (RE) of the birds. A total of 300 VICAMI® Japanese quail, housed in climatic chambers, were used from 16 weeks of age, with averages for BW of 185 g and EP of 78%, for 10 weeks. To modify the RE in the bird’s body, a qualitative dilution of dietary energy was used. Ten treatments (metabolisable energy levels) were distributed in completely randomised units, with six replicates of five quails per experimental unit. Metabolisable energy intake (MEI), egg mass (EM) and RE were expressed in kJ/kg0.67. The utilisation efficiency (kt) was estimated from the relationship between RE and MEI. The metabolisable energy for maintenance was given by RE = 0. The net energy requirement for WG was obtained from the relationship between RE in the BW as a function of the BW. The utilisation efficiency for EP (ko) was obtained from the relationship between EM and RE corrected MEI for maintenance and WG. Based on these efficiencies, the requirements for WG and EM were calculated. The energy intake by Japanese quails was partitioned according to the model: MEI = 569.8 × BW0.67 + 22 × WG + 13 × EM. The current study provides procedures and methods designed for quails as well as a simple and flexible model that can be quickly adopted by technicians and poultry companies.

Nutritionists have been discussing whether the dietary supplementation of cyst(e)ine is required as a part of the dietary methionine (Met) in the total sulfur amino acid (TSAA) requirement to achieve optimum performance in broilers. Part of Met is converted to cysteine (Cys) to meet the Cys requirement, especially for feather growth. The TSAA requirement has been determined by using graded levels of free Met in the diet, without supplementation of free cyst(e)ine. It has also been argued that the Met to Cys ratio (Met : Cys) changes with age and even with different Met sources. The objective of this study was to evaluate the two sources of Met, while determining the proportion of Met and Cys in total dietary TSAA that optimize the performance of broilers. A performance assay was carried out in a factorial arrangement (5 × 2) using 1080 broilers from 42 to 56 days of age fed diets having different dietary proportions of Met and Cys (44 : 56, 46 : 54, 48 : 52, 50 : 50 or 52 : 48) while maintaining the same dietary TSAA in the diets. Two synthetic Met sources (dl-Met or l-Met) were used for each of the diets with different dietary Met : Cys ratios. Twenty-one broilers of the same age were fed the diets 44 : 56, 48 : 52 and 52 : 48 by supplementing the diet with L-(15N) Met or L-(15N2) Cystine to study the metabolism of TSAA. No differences were observed between Met sources for feed intake, BW gain and feed conversion ratio (FCR; P > 0.05); however, FCR was numerically improved at 50 : 50 Met : Cys. Regarding TSAA utilization, the conversion of Met to Cys increased with increase in Met : Cys ratios, but the concentration of Met intermediates decreased. Broiler chickens responded to different dietary proportions of sulfur amino acids by altering their sulfur amino acid metabolism, and diets containing 50 : 50 Met : Cys is recommended for broilers of age 42 to 56 days.

Each individual in the population has a distinct maximum growth potential, and the growth curve may vary depending on the response to nutrient intake, growth phase and variability among animals. The present study aimed to (1) model weight gain (WG) response to methionine+cystine (Met+Cys) supply using different mathematical functions, (2) identify functions that better fit the growth responses of pullets, (3) determine the Met+Cys requirements that maximize WG based on breeding standards and (4) partition the Met+Cys requirements for WG and maintenance. Three trials were performed using 1448 laying-type pullets. We adopted a completely randomized design with eight treatments and six replicates. The first trial (2 to 6 weeks, P1) used 15 pullets per experimental unit. The second and third trials (8 to 12 weeks, P2; 14 to 18 weeks, P3) were used eight pullets per replicate. The Met+Cys levels were obtained using a dilution technique. The mathematical functions used to describe WG responses to Met+Cys intake were broken line, broken line with curvilinear ascendancy, Michaelis–Menten, saturation kinetics and three logistic and three exponential models. Models were selected using the Bayesian information criterion and evaluated by residual analysis. It was possible to model the responses using the studied functions. The best functions were obtained by logistic and sigmoidal models in P1 and P2, and with the broken line by the curvilinear ascendancy model in P3. The Met+Cys intake that determined the maximum potential for WG (WGmax) in P1, P2 and P3 were 313, 381 and 318 mg/day, respectively. The Met+Cys requirements for WG were 20, 22 and 27 mg/g, and for maintenance were 214, 53 and 30 mg/kgBW0.75 for P1, P2 and P3, respectively.

Quantifying robustness of farm animals is essential before it can be implemented in breeding and management strategies. A generic modelling and data analysis procedure was developed to quantify the feed intake response of growing pigs to perturbations in terms of resistance and resilience. The objective of this study was to apply this procedure to quantify these traits in 155 pigs from an experiment where they received diets with or without cereals contaminated with the mycotoxin deoxynivalenol (DON). The experimental pigs were divided equally in a control group and three DON-challenged groups. Pigs in each of the challenged groups received a diet contaminated with DON for 7 days early on (from 113 to 119 days of age), later on (from 134 to 140 days of age) or in both periods of the experiment. Results showed that the target feed intake trajectory of each pig could be estimated independently of the challenge. The procedure also estimated relatively accurately the times when DON was given to each challenged group. Results of the quantification of the feed intake response indicated that age and previous exposure to DON have an effect on the resilience capacity of the animals. The correlation between resistance and resilience traits was modest, indicating that these are different elements of robustness. The feed intake analysis procedure proved its capacity to detect and quantify the response of animals to perturbations, and the resulting response traits can potentially be used in breeding strategies.

Phosphorus (P) is an essential nutrient in livestock feed but can pollute waterways. In order for pig production to become less of a threat to the environment, excreta must contain as little P as possible or be efficiently used by plants. This must be achieved without decreasing the livestock performance. Phosphorus and calcium (Ca) deposition in the bones of growing pigs must be optimised without affecting the muscle gain. This requires precision feeding based on cutting-edge techniques of diet formulation throughout the animal growth phase. Modelling and data mining have become important tools in this quest. In this study, a mechanistic model taking into account the distribution of P between bone and soft tissues was compared to the established factorial models (INRA (Jondreville and Dourmad, 2005) and NRC (National Research Council, 2012)) that predict P (apparent total tract digestible, ATTD-P; or standardised total tract digestible, STTD-P) and Ca (total and STTD) requirements as a function of BW and protein deposition. The requirements for different bone mineralisation scenarios, namely, 100% and 85% of the genetic potential, were compared with these two models. Sobol indices were used to estimate the relative impact of growth-related parameters on mineral requirements at 30, 60 and 120 kg of BW. The INRA showed the highest value of ATTD-P requirement between 29 and 103 kg of BW (6%) and lower for lighter and higher BW. Similarly, the model for 85% bone mineralisation showed lower STTD-P requirement than NRC between 29 and 93 kg of BW (7%) and higher for lighter and higher BW. Contrary to other models, the Ca requirement of the proposed model is not fixed in relation to P. It increases from 95 kg of BW while the others decrease. The INRA showed the highest Ca requirements. The model Ca requirements for 100% bone mineralisation are higher than NRC from 20 to 38 kg of BW similar until 70 kg of BW and then higher again. For 85% objective, the model showed lower Ca requirements from 25 to 82 kg of BW and higher for lighter and higher BW. The potential Ca deposition in bones is the most sensitive parameter (84% to 100% of the variance) of both ATTD-P and Ca at 30, 60 and 120 kg. The second most sensitive parameter is the protein deposition, explaining 1% to 15% of the ATTD-P variance. Studies such as this one will help to usher in a new era of sustainable and eco-friendly livestock production.

Goat genotype may alter the net energy and protein requirements for maintenance (NEm and NPm, respectively) and weight gain (NEg and NPg).This study was designed to investigate and quantify the effect of goat type on NEm, NPm, NEg and NPg, and quantify the net requirements for energy and protein for dairy, meat and indigenous growing male goats. For that, comparative slaughter studies were gathered and a meta-analytical approach was used. Two distinct databases were organized: one composed of 233 individual records from 11 studies of meat (n = 81), dairy (n = 97) and indigenous (n = 55) growing male goats weighing from 4.50 to 51.0 kg, to depict NEm and NPm; and another database composed of 239 individual records from nine studies of meat (n = 87), dairy (n = 97) and indigenous (n = 55) growing male goats weighing from 4.30 to 51.0 kg, to depict NEg and NPg. Our findings showed that NEm of meat goats was 8.5% greater (336 ± 10.8 kJ/kg0.75 of empty BW; EBW) than dairy and indigenous goats (310 ± 8.20 kJ/kg0.75 EBW; P < 0.05). Whereas, NPm was not affected by goat type (1.92 ± 0.239 g/kg EBW; P = 0.91). The NPg was 185.1 ± 1.82 g/kg of EBW gain for goats weighing 5 kg BW and 192.5 ± 4.33 g/kg of EBW gain for goats weighing 45 kg BW, and thus did not change across goat type (P = 0.12). On the other hand, NEg increased from 7.29 ± 0.191 to 11.9 ± 0.386 MJ/kg of EBW in male dairy goats, and from 7.32 ± 0.144 to 15.7 ± 0.537 MJ/kg of EBW in meat and indigenous growing male goats weighing between 5 and 45 kg BW. When body protein was used as a predictor in the allometric equation instead of EBW seeking to account for the degree of maturity, goat type differences disappeared; however, this predictor showed a high variation among individuals. In conclusion, energy and protein requirements for gain in distinct goat types reflect on body composition differences. Future research should focus on better understanding the maturity degree and its consequences in the energy requirement of growing male goats and better depict the goat type effect on it, as well as on the efficiency of utilization.

Automated weighing systems to monitor BW and supplement intake (SI) of individual grazing cattle are being developed to better understand the seasonal nutrition and performance of grazing livestock. This study established (1) the accuracy and repeatability of a commercial walk-over weighing (WoW) system for estimating BW and (2) the accuracy of an automatic supplement weighing (ASW) unit for estimating SI based on measuring time spent at the unit. The WoW and ASW units monitored BW and SI of 112 cattle consisting of 55 cows and 57 calves grazed on a 32.5 ha paddock for 41 days, with an average of 258 BW records collected per day. Static BWs were recorded at each mustering event (n = 7) and were compared to repeated measurements collected by the WoW on the day of each mustering event. Body weight was overestimated by the WoW, with the predicted BW of calves and cows averaging 10 and 21 kg heavier, respectively, than actual, and root MS prediction errors (RMSPE) of 5.1% and 5.5% of the static BW, respectively. For both calves and cows, 38% of the MS prediction errors (MSPE) was mean bias (MB) error and 9% of MSPE was slope bias error. The concordance correlation coefficient (CCC; 0.90 v. 0.80) and modelling efficiency (MEF; 0.78 v. 0.62) of WoW BW for calves were higher than for cows, indicating that the predicted values were deviating from a 1 : 1 relationship and in particular as weight increases. A rolling average across five or more consecutive BW measures improved the accuracy of the WoW BW estimates. Regarding estimates of SI, the aggregated time the herd spent at the ASW unit was strongly associated with total SI (R2 = 0.92; P < 0.001). Further, positive linear relationships (P < 0.001) existed between cumulative weighted time spent at the ASW unit (min) and concentration of fenbendazole (FBZ) used as an intake marker and its derivatives (oxfendazole and oxfendazole sulfone) in the plasma of individual cows, with R2 of 0.54, 0.73 and 0.75, respectively. Although the WoW overestimated static BW, the low bias in the slope indicated that a linear regression model could be developed to adjust the WoW BW to reduce the MB and improve the estimate of WoW BW. The significant positive relationship between time spent at the ASW unit and individual blood FBZ concentration identified the suitability of the ASW unit for estimating SI by grazing cattle.

The description of the growth of the Japanese quails is necessary to characterize the genetic potential of these birds raised in different countries. Thus, the aim of this study was to describe the genetic potential of Japanese quails by conducting a meta-analysis considering studies conducted in different countries. Only data about the subspecies Coturnix coturnix japonica were considered; studies regarding Coturnix coturnix coturnix were not examined. The criteria investigated were BW (W), age (t), year of publication and location of the study. Each set of genetic material within a publication was coded as one study. The Gompertz function was used to interpret the growth of laying quails; thus, each study was represented by Gompertz parameters. The W and t data were applied to estimate the values of Gompertz growth parameters, including BW at maturity (Wm), BW at birth (Wi), maturity rate (B) and inflection point (IP). The age at which the maximum growth rate was achieved (t*) was calculated considering the parameters Wm, Wi and B. To estimate these parameters, random regression was used to randomize the parameter Wm. The parameters estimated for each assay were used in exploratory, grouping, and principal component analyses. The values of Wi ranged from 4.1 to 11.6 g. The values of B ranged from 0.0393 to 0.1039/day, and consequently, the values of t* and IP ranged from 14 to 31 days and 9.21 to 31.03 g, respectively. These results show that there is considerable variability in the growth potential of Japanese quails. To better understand this variation, two groups were examined: Brazil and other countries, according to the grouping of Wi, Wm, B and t*; parameter B was the variable that presented the highest specificity, indicating that both groups modified the maturity rate. For the principal component analysis, the year of publication showed a relationship with the growth parameters but only for studies performed in Brazil. For studies carried out in other countries, the changes in growth parameters were not related to the year of publication. In Brazilian studies, there was a decrease in the maturity rate, but the weight at maturity was higher. Therefore, it appears that different strategies of genetic selection were adopted in Brazil compared to other countries.

European pig production faces economic and environmental challenges. Modelling can help farmers simulate and understand how changes in their management practices affect the efficiency of their production system. We developed an individual-based model of a pig-fattening unit that considers individual variability in performance among pigs, farmers’ feeding practices and animal management and estimates environmental impacts (using life cycle assessment) and economic results of the unit. We previously demonstrated that this model provides reliable estimates of farm performance for different combinations of management practices, pig types and building characteristics. The objectives of this study were to quantify how interactions between feeding practices and animal management influence fattening unit results in healthy or impaired health conditions using the model. A virtual experiment was designed to evaluate effects of interactions between feeding practices, health status of the pig herd and infrastructure constraints on the technical performance, economic results and environmental impacts of the unit. The virtual experiment consisted of 96 scenarios, which combined chosen values of 6 input parameters of the model: batch interval (35 days and 7 days), use or non-use of a buffer room to manage the lightest pigs, feed rationing (ad libitum and restricted) and sequence plans (two-phase (2P), daily-phase (DP)), scale at which the feeding plan is applied (i.e. room, pen and individual) and health status of the pig herd (i.e. healthy v. impaired). Variance analysis was used to test effects of the factors in these 96 scenarios, and multivariate data analyses were used to classify the scenarios. Healthy populations obtained on average higher economic results (e.g. gross margin of 11.20 v. 1.50 €/pig) and lower environmental impacts (e.g. 2.24 v. 2.38 kg CO2-eq/kg pig live weight gain) than the population with impaired health. With 35 days batch interval and DP feeding, populations with impaired health reached gross margin similar to healthy populations with 2P ad libitum feeding and 7 days batch interval. Restricted, DP and individual feeding plans improved the economic and environmental performances of the unit for both health statuses. This study highlighted that health status of the pig herd is the main factor that affects technical, economic and environmental performances of a pig-fattening unit, and that adequate feeding strategies and animal management can compensate, to some extent, the effects of impaired health on environmental impacts but not on gross margin.

Precision feeding using real-time models to estimate daily tailored diets can potentially increase nutrient utilization efficiency. However, to improve the estimation of amino acid requirements for growing–finishing pigs, it is necessary to accurately estimate the real-time body protein (BP) mass. The aim of this study was to predict individual BP over time in order to obtain individual daily protein content of the gain (i.e., protein deposition/daily gain, PD/DG) to be integrated into a real-time model used for precision feeding. Two databases were used in this study: one for the development of the equations for the model and the other for model evaluation. For the equations, data from 79 barrows (25 to 144 kg BW) were used to estimate the parameters for a Gompertz function and a mixed linear-quadratic regression. Individual BP predictions obtained by dual X-ray absorptiometry were regressed as a function of BW. Individual pig BP estimates were obtained by linear-quadratic regression using the MIXED procedure of SAS, considering pig measurements repeated in time. Individual Gompertz curves were obtained using the NLMIXED procedure of SAS. Both procedures generate an average or a general model, which was assessed for accuracy with the database used to generate the equations. Coefficients of concordance and determination were both 0.99, and the RMSE was 0.21 kg for the linear-quadratic regression. The Gompertz curve coefficients of concordance and determination were both 0.99, and the RMSE was 0.36 kg. In sequence, the linear-quadratic regression and Gompertz curve were evaluated in an independent data set (488 observations; 21 to 126 kg BW). The linear-quadratic regression to predict BP mass was accurate (mean absolute percentage error (MAPE) = 2.5%; bias = 0.03); the Gompertz model performed worse (MAPE = 3.9%; bias = 0.04) than the linear-quadratic regression. When using the derivative of these equations to predict PD/DG, the linear-quadratic regression was more accurate (MAPE = 4.8%, bias = 0.17%) compared to the Gompertz (MAPE = 10.6%, bias = −0.99%) mainly due to the linear decrease in PD/DG in the observed data. Further analysis using individual pig data showed that the goodness of fit of PD/DG curve depends on the individual shape of the growth curve, with either the Gompertz or the linear-quadratic regression being more accurate for specific individuals. Therefore, both approaches are provided to allow end users to select the model that best fits their needs. The proposed update of the empirical component of the original model, using either linear-quadratic regression or the Gompertz function, is able to predict BP in real-time with good accuracy.

Pigs exposed to stressors might change their daily typical feeding intake pattern. The objective of this study was to develop a method for the early identification of deviations from an individual pig’s typical feeding patterns. In addition, a general approach was proposed to model feed intake and real-time individual nutrient requirements for pigs with atypical feeding patterns. First, a dynamic linear model (DLM) was proposed to model the typical daily feed intake (DFI) and daily gain (DG) patterns of pigs. Individual DFI and DG dynamics are described by a univariate DLM in conjunction with Kalman filtering. A standardized tabular cumulative sum (CUMSUM) control chart was applied to the forecast errors generated by DLM to activate an alarm when a pig showed deviations from its typical feeding patterns. The relative feed intake (RFI) during a challenge period was calculated. For that, the forecasted individual pig DFI is expressed as its highest DFI relative to the intake during pre-challenge period. Finally, the DLM and RFI approaches were integrated into the actual precision-feeding model (original model) to estimate real-time individual nutrient requirements for pigs with atypical feeding patterns. This general approach was evaluated with data from two studies (130 pigs, at 35.25 ± 3.9 kg of initial BW) that investigated during 84 days the effect of precision-feeding systems for growing-finishing pigs. The proposed general approach to estimating real-time individual nutrient requirements (updated model) was evaluated by comparing its estimates with those generated by the original model. For 11 individuals out of 130, the DLM did not fit the observed data well in a specific period, resulting in an increase in the sum of standardized forecast errors and in the number of time steps that the model needed to adapt to the new patterns. This poor fit can be identified by the increase in the CUMSUM with a consequent alarm generated. The results of this study show that the updated model made it possible to reduce intra-individual variation for the estimated lysine requirements in comparison with the original model, especially for individuals with atypical feeding patterns. In conclusion, the DLM in conjunction with CUMSUM could be used as a tool for the online monitoring of DFI for growing-finishing pigs. Moreover, the proposed general approach allows the estimation of real-time amino acid requirements and accounts for the reduced feed intake and growth potential of pigs with atypical feeding patterns.

The main objective of this study was to develop a dynamic energy balance model for dairy goats to describe and quantify energy partitioning between energy used for work (milk) and that lost to the environment. Increasing worldwide concerns regarding livestock contribution to global warming underscore the importance of improving energy efficiency utilization in dairy goats by reducing energy losses in feces, urine and methane (CH4). A dynamic model of CH4 emissions from experimental energy balance data in goats is proposed and parameterized (n = 48 individual animal observations). The model includes DM intake, NDF and lipid content of the diet as explanatory variables for CH4 emissions. An additional data set (n = 122 individual animals) from eight energy balance experiments was used to evaluate the model. The model adequately (root MS prediction error, RMSPE) represented energy in milk (E-milk; RMSPE = 5.6%), heat production (HP; RMSPE = 4.3%) and CH4 emissions (E-CH4; RMSPE = 11.9%). Residual analysis indicated that most of the prediction errors were due to unexplained variations with small mean and slope bias. Some mean bias was detected for HP (1.12%) and E-CH4 (1.27%) but was around zero for E-milk (0.14%). The slope bias was zero for HP (0.01%) and close to zero for E-milk (0.10%) and E-CH4 (0.22%). Random bias was >98% for E-CH4, HP and E-milk, indicating non-systematic errors and that mechanisms in the model are properly represented. As predicted energy increased, the model tended to underpredict E-CH4 and E-milk. The model is a first step toward a mechanistic description of nutrient use by goats and is useful as a research tool for investigating energy partitioning during lactation. The model described in this study could be used as a tool for making enteric CH4 emission inventories for goats.

Until recently, beef carcass payment grids were predominantly based on weight and fatness categories with some adjustment for age, defined as number of adult teeth, to determine the price received by Australian beef producers for slaughter cattle. With the introduction of the Meat Standards Australia (MSA) grading system, the beef industry has moved towards payments that account for intramuscular fat (IMF) content (marble score (MarbSc)) and MSA grades. The possibility of a payment system based on lean meat yield (LMY, %) has also been raised. The BeefSpecs suite of tools has been developed to assist producers to meet current market specifications, specifically P8-rump fat and hot standard carcass weight (HCW). A series of equations have now been developed to partition empty body fat and fat-free weight into carcass fat-free mass (FFM) and fat mass (FM) and then into flesh FFM (FleshFFM) and flesh FM (FleshFM) to predict carcass components from live cattle assessments. These components then predict denuded lean (kg) and finally LMY (%) that contribute to emerging market specifications. The equations, along with the MarbSc equation, are described and then evaluated using two independent datasets. The decomposition of evaluation datasets demonstrates that error in prediction of HCW (kg), bone weight (BoneWt, kg), FleshFFM (kg), FleshFM (kg), MarbSc and chemical IMF percentage (ChemIMF%) is shown to be largely random error (%) in evaluation dataset 1, though error for ChemIMF% was primarily slope bias (%) in evaluation dataset 1, and BoneWt had substantial mean bias (%) in evaluation dataset 2. High modelling efficiencies of 0.97 and 0.95 for predicting HCW for evaluation datasets 1 and 2, respectively, suggest a high level of accuracy and precision in the prediction of HCW. The new outputs of the model are then described as to their role in estimating MSA index scores. The modelling system to partition chemical components of the empty body into carcass components is not dependent on the base modelling system used to derive empty body FFM and FM. This can be considered a general process that could be used with any appropriate model of body composition.